Morphomolecular Longitudinal Endoscopy of Carcinogenesis

Abstract

Current diagnostic approaches for head and neck cancer rely primarily on white-light endoscopy and tissue biopsy, providing only static, episodic assessments that fail to capture dynamic changes in tumour biology over time. This absence of continuous, detailed molecular and microscopic structural information makes early identification of dysplasia or cancer difficult and can delay critical treatment. Here, we introduce a unified, label-free endoscopic platform that combines Raman spectroscopy (RS) and optical coherence tomography (OCT) within a miniaturised, forward-viewing fibre-optic probe. This integration enables simultaneous video-rate OCT imaging and subsecond RS acquisition linking morphological and molecular tissue trajectories in vivo. We developed an explainable AI-based fusion framework that learns joint temporal representations of biochemical and structural features to classify tissue states while highlighting the most influential predictors. In a longitudinal 4NQO-induced murine model of carcinogenesis, the system tracked epithelial transformation in real-time from hyperplasia through dysplasia to neoplasia. By capturing temporally resolved morphomolecular trajectories in vivo, this platform establishes a new paradigm for early cancer detection and real-time surveillance, supporting timely and individualised clinical decision-making.